{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = r'D:\\Users\\VonBrank\\Documents\\GitHub\\code-learning\\algorithm\\deep-learning\\computer-visualization\\cs231n\\datasets\\cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "**Note: For the three distance computations that we require you to implement in this notebook, you may not use the np.linalg.norm() function that numpy provides.**\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 1** \n",
    "\n",
    "Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 137 / 500 correct => accuracy: 0.274000\n"
     ]
    }
   ],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 139 / 500 correct => accuracy: 0.278000\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 2**\n",
    "\n",
    "We can also use other distance metrics such as L1 distance.\n",
    "For pixel values $p_{ij}^{(k)}$ at location $(i,j)$ of some image $I_k$, \n",
    "\n",
    "the mean $\\mu$ across all pixels over all images is $$\\mu=\\frac{1}{nhw}\\sum_{k=1}^n\\sum_{i=1}^{h}\\sum_{j=1}^{w}p_{ij}^{(k)}$$\n",
    "And the pixel-wise mean $\\mu_{ij}$ across all images is \n",
    "$$\\mu_{ij}=\\frac{1}{n}\\sum_{k=1}^np_{ij}^{(k)}.$$\n",
    "The general standard deviation $\\sigma$ and pixel-wise standard deviation $\\sigma_{ij}$ is defined similarly.\n",
    "\n",
    "Which of the following preprocessing steps will not change the performance of a Nearest Neighbor classifier that uses L1 distance? Select all that apply.\n",
    "1. Subtracting the mean $\\mu$ ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu$.)\n",
    "2. Subtracting the per pixel mean $\\mu_{ij}$  ($\\tilde{p}_{ij}^{(k)}=p_{ij}^{(k)}-\\mu_{ij}$.)\n",
    "3. Subtracting the mean $\\mu$ and dividing by the standard deviation $\\sigma$.\n",
    "4. Subtracting the pixel-wise mean $\\mu_{ij}$ and dividing by the pixel-wise standard deviation $\\sigma_{ij}$.\n",
    "5. Rotating the coordinate axes of the data.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('One loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No loop difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('No loop difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "no_loop",
    "scrolled": true,
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 24.983689 seconds\n",
      "One loop version took 38.931001 seconds\n",
      "No loop version took 0.206878 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# You should see significantly faster performance with the fully vectorized implementation!\n",
    "\n",
    "# NOTE: depending on what machine you're using, \n",
    "# you might not see a speedup when you go from two loops to one loop, \n",
    "# and might even see a slow-down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.239000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.240000\n",
      "k = 3, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.254000\n",
      "k = 5, accuracy = 0.248000\n",
      "k = 5, accuracy = 0.266000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.292000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 8, accuracy = 0.262000\n",
      "k = 8, accuracy = 0.282000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 8, accuracy = 0.290000\n",
      "k = 8, accuracy = 0.273000\n",
      "k = 10, accuracy = 0.265000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.276000\n",
      "k = 10, accuracy = 0.284000\n",
      "k = 10, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.260000\n",
      "k = 12, accuracy = 0.295000\n",
      "k = 12, accuracy = 0.279000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.252000\n",
      "k = 15, accuracy = 0.289000\n",
      "k = 15, accuracy = 0.278000\n",
      "k = 15, accuracy = 0.282000\n",
      "k = 15, accuracy = 0.274000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.282000\n",
      "k = 20, accuracy = 0.285000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "X_train_folds = np.split(X_train, num_folds)\n",
    "y_train_folds = np.split(y_train, num_folds)\n",
    "# print(X_train_folds[1].shape)\n",
    "# print(y_train_folds[1].shape)\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = np.zeros(num_folds)\n",
    "    acc = []\n",
    "    for i in range(0, num_folds):\n",
    "        X_tr = X_train_folds[: i] + X_train_folds[i+1 :]\n",
    "        y_tr = y_train_folds[: i] + y_train_folds[i+1 :]\n",
    "        X_tr = np.concatenate(X_tr, axis=0)\n",
    "        y_tr = np.concatenate(y_tr, axis=0)\n",
    "        classifier = KNearestNeighbor()\n",
    "        classifier.train(X_tr, y_tr)\n",
    "        X_cv = X_train_folds[i]\n",
    "        y_cv = y_train_folds[i]\n",
    "        y_cv_pred = classifier.predict(X_cv, k=k, num_loops=0)\n",
    "        num_correst = np.mean(y_cv_pred == y_cv)\n",
    "        acc.append(num_correst)\n",
    "    k_to_accuracies[k] = acc\n",
    "# pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "cross_validation"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 10\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question 3**\n",
    "\n",
    "Which of the following statements about $k$-Nearest Neighbor ($k$-NN) are true in a classification setting, and for all $k$? Select all that apply.\n",
    "1. The decision boundary of the k-NN classifier is linear.\n",
    "2. The training error of a 1-NN will always be lower than that of 5-NN.\n",
    "3. The test error of a 1-NN will always be lower than that of a 5-NN.\n",
    "4. The time needed to classify a test example with the k-NN classifier grows with the size of the training set.\n",
    "5. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n"
   ]
  }
 ],
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